Relation Therapeutics, a biotechnology company at the forefront of leveraging computational and experimental approaches to drug discovery, today announced $35 million in new seed financing, bringing its total seed fundraise to $60 million.
This latest round was led by DCVC and co-led by NVIDIA’s venture arm, NVentures, with participation from initial seed lead Magnetic Ventures, existing investors Khosla Ventures, and Abcam founder Jonathan Milner, and new investors ARK Invest and Deerfield Management Company.
David Roblin, CEO of Relation Therapeutics, said: “This financing is a significant milestone for Relation Therapeutics and underscores the confidence our investors have in our innovative approach to drug discovery and development. Our technology platform, uniquely integrated with deep R&D expertise, places us in an outstanding position to transform drug discovery. This new capital allows us to accelerate our discovery of novel biology, targets and medicines: we are immensely excited about our progress and the impact we can make for patients.”
Led by experienced drug developers, computational scientists and entrepreneurs, the company’s technologies enable it to generate, analyse and interpret human data about the behaviour of genes, cells and tissues in an unparalleled fashion. Relation’s Lab-in-the-Loop seamlessly integrates single-cell analysis, genomics and machine learning to build rich maps of disease biology. From this, the company discovers novel targets and subsequently develops medicines. The funds raised will enable Relation to advance its osteoporosis pipeline towards the clinic, as well as initiate discovery campaigns in new therapeutic areas of high unmet need.
“We see Relation as a leader in the emerging techbio economy,” said Jason Pontin, General Partner at DCVC, who sits on Relation’s board. “The successful techbio companies will be those who build a defensible advantage by generating world-class datasets about the biology of interest, and who apply the latest advances in large AI models with unique guardrails to extract actionable and credible predictions that guide wet-lab work—all in the service of transforming drug discovery. Relation is swiftly proving out that strategy for osteoporosis as a first indication, and we can’t wait for them to bring their advantages to bear on other disease areas in their pipeline.”
“By harnessing generative AI, drug discovery teams can design novel molecules with the desired properties for new medicines,” said Mohamed “Sid” Siddeek, corporate vice president and head of NVentures. “Relation is a leader in this approach, helping set the foundation for new drug discoveries to benefit patients around the world.”
“Magnetic backed Relation early due to our strong belief in the power of their pioneering technology platform, which combines the most advanced ML with omics, and our confidence in the team,” said Christine Aylward, Founder, Magnetic Ventures. “Traditional pathways of drug discovery and development have been marred by inefficiencies and a lack of predictability. Leveraging a ‘Lab-in-the-Loop’ strategy, Relation’s approach exemplifies the transformative potential of AI and marks a milestone in the journey towards innovative drug development. We are thrilled to see Relation’s progress, and the addition of these new strategic investors.”
As a first indication, Relation is developing treatments for osteoporosis, a disease that affects over 45 million patients across the US, Europe and Japan. As part of these efforts, Relation has built considerable proprietary data resources, including Osteomics, a clinical observational study to create the world’s largest functional, single-cell bone atlas derived from human patient tissue.
Charles Campbell Roberts, Co-founder and Chair of Relation Therapeutics said: “This investment offers more than just funding; it brings on board key strategic development partners NVIDIA, Deerfield Management Company and ARK Invest, along with the expertise of our top-tier institutional investors. The round will catalyse our ability to unearth causal biology using machine learning, including our LLMs adapted for biology, and accelerate the development of new therapies for patients.”
